YOLO object detection model? - tensorflow

Question

Currently, I am reading the Yolo9000 model "https://arxiv.org/pdf/1612.08242.pdf" and I am very confused about how the model can predict the bounding box for object detection, I did many examples with Tensorflow, and in most of them we give to the model "Images and Label of images".
My questions are:
1- How we can pass the bounding box instead of labels to the model?
2- How can the the model learn that many boxes belong to one images?

Answer 1

In YOLO, we divide the image into 7X7 grid. For each of the grid location, the network predicts three things -
Probability of an object being present in that grid
If an object lies in this grid, what would be the co-ordinates of the
bounding box?
If an object lies in this grid, which class does it
belong to?
If we apply regression for all the above variables for all 49 grid locations, we will be able tell which grid locations have objects(using first parameter). For the grid locations that have objects, we can tell the bounding box co-ordinates and correct class using the second and third parameters.
Once we have designed a network that can output all the information we need, prepare the training data in this format i.e. find these parameters for every 7X7 grid location in every image in your dataset. Next you simply train the deep neural network to regress for these parameters.

Answer 2

To pass bounding boxes of an image we need to create it first. You can create bounding boxes for any image using specific tools. Here, you have to create boundaries that bound an object within it and then label that bounding box/rectangle. You to do this for every object in the image you want your model to train/recognize.
There is one very useful project in this link, you should check that out if you need to understand about bounding boxes.
I have just started learning object detection with tensorflow. So as and when I get proper info on providing bounding boxes to the object detection model I'll also update that here. Also if you have solved this problem by now, you can also provide the details to help out others facing same kind of problems.

Answer 3

1- How we can pass the bounding box instead of labels to the model?
If we want to train a model that performs object detection (not object classification), we have to pass the truth labels as .xml files, for example. An xml file contains information about objects that exist in an image. Each information about object is composed of 5 values:
class name of this object, such as car or human...
xmin: x coordinate of the box's top left point
ymin: y coordinate of the box's top left point
xmax: x coordinate of the box's bottom right point
ymax: y coordinate of the box'x bottom right point
One bounding box within an image is specified as a set of 5 values like above. If there are 3 objects in an image, the xml file will contain 3 sets of this values.
2- How can the the model learn that many boxes belong to one images?
As you know, the output of YOLOv2 or YOLO9000 has shape (13, 13, D), where D depends on how many class of object you're going to detect. You can see that there are 13x13 = 169 cells (grid cells) and each cell as D values (depth).
Among 169 grid cells, there are some grid cells that are responsible to predict bounding boxes. If the center of a true bounding box falls on a grid cell, this grid cell is responsible to predict that bounding box, when it is given the same image.
I think there must be a function that reads the xml annotation files and determines which grid cells are responsible to detect bounding boxes.
To make the model learn the box positions and shapes not only the classes, we have to build an appropriate loss function. The loss function used in YOLOv puts cost also on the box shapes and positions. So the loss is calculated as the weighted sum of the following individual loss values:
Loss on the class name
Loss on the box position (x-y coordinates)
Loss on the box shape (box width and height)
SIDE NOTE:
Actually, one grid cell can detect up to B boxes, where B depends on
implementations of YOLOv2. I used darkflow to train YOLOv2 on my
custom training data, in which B was 5. So the model can detect 169*B
boxes in total, and loss is the sum of 169*B small losses.
D = B*(5+C), where C is the number of classes you want to detect.
Before passed to the model, the box shapes and positions are
converted into relative values to the image size.